Machine Learning and its types with application

Machine Learning and its types

1. MACHINE LEARNING

2. INTRODUCTION REINFORCEMENT LEARNING SUPERVISED LEARNING UNSUPERVISED LEARNING CONTENT

3. WHAT IS AI ? Artificial intelligence, refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. It encompasses a wide range of techniques and technologies aimed at enabling computers to perform tasks that typically require human intelligence, such as understanding natural language, recognizing patterns, making decisions, and solving problems.

4. WHAT IS ML? MACHINE LEARNING IS A SUBSET OF ARTIFICIAL INTELLIGENCE (AI) THAT FOCUSES ON DEVELOPING ALGORITHMS AND MODELS THAT ALLOW COMPUTERS TO LEARN FROM DATA AND MAKE PREDICTIONS OR DECISIONS WITHOUT BEING EXPLICITLY PROGRAMMED TO PERFORM SPECIFIC TASKS. IN TRADITIONAL PROGRAMMING, HUMANS WRITE CODE TO INSTRUCT A COMPUTER ON HOW TO PERFORM A TASK. HOWEVER, IN MACHINE LEARNING, THE COMPUTER LEARNS TO PERFORM TASKS BY ANALYSING AND INTERPRETING DATA.

5. Machine learning algorithms are trained to find relationships and patterns in data. They use historical data as input to make predictions, classify information, cluster data points, reduce dimensionality and even help generate new content , as demonstrated by new ML-fueled applications such as ChatGPT, Dall-E 2 and GitHub Copilot.

6. ML Process The Machine Learning process involves building a Predictive model that can be used to find a solution for a Problem Statement Step 1: Problem Definition Step 2: Data Collection Step 3: Preparing the Data Step 4: Exploratory Data Analysis Step 5: Building the Machine Learning Model Step 6: Model evaluation and optimization Step 7: Predictions

7. TYPES OF MACHINE LEARNING

8. SUPERVISED LEARNING

9. Supervised machine learning is a type of machine learning where the algorithm learns from labeled data, which means it is provided with input-output pairs during the training process. The goal is to learn a mapping from input variables to output variables, allowing the algorithm to make predictions or decisions when presented with new, unseen data This learning model takes direct feedback to check whether it is producing correct output or not.

10. I n supervised learning, each example in the training dataset consists of an input and a corresponding output label. The input is typically represented as a feature vector, where each feature provides some information about the example. The output label is the target variable that the algorithm aims to predict. LABELED DATA

11. TRAINING PROCESS During the training phase, the algorithm is presented with a dataset containing input output pairs. It learns from the examples by adjusting its internal parameters to minimize the error between its predictions and the true labels. The learning algorithm iteratively improves its performance through processes like gradient descent, where it updates its parameters in the direction that reduces the prediction error.

12. Conclusion The ultimate goal of supervised learning is to generalize well to unseen data. A model that performs well on the training data but poorly on new, unseen data is said to overfit. Overfitting occurs when the model captures noise in the training data rather than the underlying pattern. Techniques such as cross-validation, regularization, and early stopping are used to prevent overfitting and encourage better generalization. Examples Classification: Email spam detection, sentiment analysis, image recognition. Regression: Stock price prediction, house price estimation, demand forecasting.

13. Linear Regression: Simple and widely used for regression tasks. Logistic Regression: Used for binary classification problems. Decision Trees: Versatile for both classification and regression tasks. Support Vector Machines (SVM): Effective for classification tasks, especially when dealing with high- dimensional data. Neural Networks: Deep learning models capable of learning complex patterns from large datasets. COMMON ALGORITHMS

14. Unsupervised Learning

15. Unsupervised learning is a type of machine learning where the model learns patterns from unlabeled data without explicit guidance. Purpose: Uncover hidden patterns, group similar data points, and reduce the dimensionality of data.

16. CLUSTERING ASSOCIATION Clustering is the method of dividing the objects into clusters that are similar between them and are dissimilar to the objects belonging to another cluster. For example, finding out which customers made similar product purchases. Association is a rule-based machine learning to discover the probability of the co-occurrence of items in a collection. For example, finding out which products were purchased together.

17. Customer Segmentation: Divide customers into groups based on their purchasing behavior. Image and Text Analysis: Group similar images or classify text documents without labeled data. Recommendation Systems: Recommend products or content based on user behavior and preferences. Applications of Unsupervised Learning

18. • Subjectivity in evaluation: Unlike supervised learning, where performance can be objectively measured against labeled data, evaluating the performance of unsupervised learning algorithms often relies on subjective measures such as clustering coherence or visual inspection. This subjectivity can make it challenging to compare different algorithms or determine the optimal solution. • Difficulty in interpreting results: Unsupervised learning algorithms often provide clusters, patterns, or associations in the data without explicit explanations. Interpreting these results can be challenging, especially in high-dimensional or complex datasets, leading to potential misinterpretation or misunderstanding of the underlying structure. Challenges and Limitations

19. REINFORCMENT LEARNING

20. REINFORCMENT LEARNING Reinforcement learning in machine learning is like a trial-and-error learning process. It’s similar to training a pet: when the pet does something good, it gets a treat, and when it does something bad, it doesn’t. Over time, the pet learns to repeat the good behaviors to get more treats.

21. FUNDAMENTALS OF REINFORCEMENT LEARNNG In reinforcement learning, an AI agent (like a robot or software) learns to make decisions by performing actions and getting rewards or penalties based on the results. The agent isn’t told what to do but instead learns from its experiences, trying to get as many rewards as possible. The balance between exploration(seeking new knowledge) exploitation(using existing knowledge) is a key challenge in reinforcement learning.Strategies such as epsilon-greedy and UCB adress this trade-off.

22. Deep reinforcement learning combines deep learning with reinforcement learning,enabling the handling of complex,Q- networks and policy gradients are popular approaches in this domain Reinforcement learning has revolutionized robotics by enabling autonomous decision-making in dynamic environments. From robotic arm control to autonomous navigation,RL has diverse applications in this field. DEEP REINFORCEMENT LEARNING

23. Applications of Reinforcement Autonomous Vehicles: Guides decisions for self-driving cars, including lane changes and obstacle avoidance. Robotics: Used in motion control for navigating and manipulating objects. Game Playing: Excels in training AI for complex games like Go and chess. Natural Language Processing (NLP): Applies to text summarization and question-answering for human-like text generation. Personalized Recommendations: Enhances user experience through tailored content recommendations.

24. Pros Cons 1.Complex problem solving: It can solve very complex problem 2.Error correction : Capable of correcting errors during training. 3.Performance maximization : Intended to maximizing performance within a specific context. 1.Maintenance cost : High maintenance cost due to complexity. 2. Complexity for simple problems not preferable for solving simple problems. Thankyou...